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intelligence Apply for this job See advertisement About the position Inland School of Business and Social Sciences , The PhD program Innovation in Services in the Public and Private Sectors (INSEPP) , invites
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strong mix of experimental and analytical skills, and the ability to communicate complex technical ideas. Qualifications: • A PhD in Electrical Engineering, Computer Engineering, Computer
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. in Computer Science, Computer Engineering, Information Science, Robotics or any other related field. The position requires experience with at least one of the following: Data Science, Machine Learning
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Australian National University | Canberra, Australian Capital Territory | Australia | about 11 hours ago
approaches to model uncertainty for learned computer vision systems, including dense prediction. The position will develop novel methods for deep learning in computer vision that accurately quantify their own
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About the Opportunity We seek to hire a Postdoctoral Research Fellow (PRF) through the Northeastern University-Industrial Fellowship program with Forma, LLC. The PRF will be responsible
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Requirements below) demonstrating: compelling future vision (including potential follow-on opportunities), collaborative approach (connection to/ building on efforts already underway), and broader positive
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teaching assistance as a guest lecturer. If you're ready to utilize your expertise and collaborate with a dynamic team, we invite you to apply. Minimum Required Qualifications Education: Appropriate PhD in
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, and Simultaneous Localization and Mapping (SLAM) is desired. The position is open to PhDs with background in robotics, controls, AI, and/or computer vision. The candidate is expected to work in a highly
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. We are looking for someone who shares our values and who will support the mission of the university through their work. Qualifications: PhD in Machine Learning, Computer Science, or a related field by
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on two core but complementary areas: Computer vision and sensor data analysis, applied to tasks such as object detection in drone images (e.g., pest or disease detection), object tracking (e.g. leaves